Large-scale diffusion-based generative models have led to breakthroughs in
text-conditioned high-resolution image synthesis. Starting from random noise,
such text-to-image diffusion models gradually synthesize images in an iterative
fashion while conditioning on text prompts. We find that their synthesis
behavior qualitatively changes throughout this process: Early in sampling,
generation strongly relies on the text prompt to generate text-aligned content,
while later, the text conditioning is almost entirely ignored. This suggests
that sharing model parameters throughout the entire generation process may not
be ideal. Therefore, in contrast to existing works, we propose to train an
ensemble of text-to-image diffusion models specialized for different synthesis
stages. To maintain training efficiency, we initially train a single model,
which is then split into specialized models that are trained for the specific
stages of the iterative generation process. Our ensemble of diffusion models,
called eDiff-I, results in improved text alignment while maintaining the same
inference computation cost and preserving high visual quality, outperforming
previous large-scale text-to-image diffusion models on the standard benchmark.
In addition, we train our model to exploit a variety of embeddings for
conditioning, including the T5 text, CLIP text, and CLIP image embeddings. We
show that these different embeddings lead to different behaviors. Notably, the
CLIP image embedding allows an intuitive way of transferring the style of a
reference image to the target text-to-image output. Lastly, we show a technique
that enables eDiff-I's "paint-with-words" capability. A user can select the
word in the input text and paint it in a canvas to control the output, which is
very handy for crafting the desired image in mind. The project page is
available at this https URL

该研究提出了一种基于扩散的生成模型，通过设计针对迭代生成过程的特殊模型，实现了更好的文本对齐，利用不同嵌入技术对模型进行调整，实现对参考图像风格进行自适应转换，并展示了一种 “文字涂鸦” 的技术，可帮助用户控制所需的图像输出。